LawAI Intelligence
Platform

Most models tokenize a PDF left-to-right and hope for the best. Ours doesn't.

Our proprietary extraction engine performs layout-aware parsing: it identifies and separately extracts document headers, section titles, clause bodies, footnotes, signature blocks, and defined terms - treating each as a distinct, typed data object, not a continuous stream of text.

Scanned PDFs run through our OCR layer first, trained specifically on legal typography, court formatting, and handwritten annotations.

The result: a structured legal document object - not a text dump.

Standard pipelines run a single embedding model across the entire document. We don't.

We run separate, specialized embedding models for separate document layers:

• → Title & section header embedderTrained on legal document taxonomy — understands that "Representations and Warranties" in an SPA is structurally different from the same phrase in an NDA.
• → Clause body embedderLine-by-line embedding of clause content. Each line is embedded independently, then grouped into clause-level semantic units. This preserves intra-clause meaning that chunk-based embedders destroy.
• → Entity & obligation embedderSeparately embeds extracted entities — party names, dates, monetary values, governing law, defined terms — as typed structured objects alongside their vector representations.

Why? Because a query about a termination clause should retrieve termination clauses — not every paragraph that mentions 'termination' in a footnote or recital.

Our embedding models run on Modal - a serverless GPU inference layer.

Each firm's embedding pipeline spins up in its own isolated serverless container. No shared compute. No shared memory. One firm's documents never touch another firm's inference environment.

Cold start is under 400ms. Embedding a 200-page contract takes under 6 seconds. The container terminates after the job completes - nothing persists.

At scale, cosine similarity alone is not a retrieval strategy. With 40,000+ documents in a data room, a pure vector sweep returns semantically similar text from the wrong document type, wrong date, wrong party.

Our retrieval layer applies structured pre-filters before vector search runs:

Vector search then runs only within the filtered candidate set - a fraction of the full corpus, precisely scoped to the query context.

This is why our retrieval precision holds at 99.9% even across 50,000-document data rooms where standard RAG pipelines degrade to ~60%.

Our workflow agents sit above the vector layer. Each agent has a domain-specific retrieval interface - the Contract Intake Agent queries differently than the Due Diligence Agent.

Rather than issuing raw vector queries, agents issue typed retrieval requests:

This resolves to a filtered vector query + a structured metadata lookup, merged and ranked before the LLM ever sees it.

The LLM receives clean, scoped, typed context. Not a blob of retrieved text. This is why it doesn't hallucinate.